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A novel binary successive approximation-based evolutionary search strategy has been proposed to solve the economic-emission load dispatch problem by searching the generation pattern of committed units. Inequality constraints are taken care of during the search of a generation pattern. To meet the demand, a slack generator is introduced to compensate the perturbation of unmet load during the search. To determine the trade-off relationship between conflicting objectives in the non-inferior domain, the weighting method is exploited. Once the trade-off has been obtained, fuzzy set theory helps the decision maker to choose the optimal operating point over the trade-off curve and adjust the generation schedule in the most preferred manner. Generally the weights are regulated in a systematic manner, which is a time-consuming process. To reduce the time to arrive at the best compromising solutions, the weight pattern assigned to objectives are searched for more significant digits in a fixed number of iterations. Performance of the algorithm is investigated on economic load dispatch problems of different sizes and complexity having non-convex cost curves where conventional gradient-based methods are inapplicable. This technique has emerged as the useful optimisation tool for handling network losses, ramp rate limits, valve point loading and prohibited zone avoidance into account to determine the global optimal dispatch solution as well as optimal operating point in the non-inferior domain for any number of the goals. The method is applied on various test systems and better results are achieved with reference to emission (Kg/h), however when compared with other techniques, the proposed method has lower performance with reference to losses (MW) and cost ($/h).